Using Traffic Diversities for Scheduling Wireless Interfaces for Energy Harvesting in Wireless Devices
The data traffic in many cases is specifying the expectations and the encouragement of new Mobile services/M-services (Location-based etc) and embosoms, through the user-centered awareness, to expose an innovative range of on-the-move applications. As today two distinct domains exist, the wireless world and the Internet world where, both can be met over a traffic-oriented framework providing reliable end-to-end users’ connectivity and exchange of resources. The need to allocate and balance resources among different traffic classes to accomplish the best usage of network resources while maintaining the topology and the wireless connectivity of the users is today even more timely. The user’s movements affect the type of connectivity, thus aggravating the degree of cooperation among users and degrading the reliability of communication. Traffic diversities are being considered in this chapter taking into consideration the traffic impact on the energy conservation of the nodes that are changing their location according to certain pattern as well as the consideration of the traffic as a feedback mechanism to prolong network’s lifetime and nodes lifetime and communication duration extensibility. The chapter covers the primary traffic techniques and methodologies in order to show the direct dependencies between traffic and wireless interfaces’ scheduling mechanisms as well as exposing the power-related parameters during the resource exchange process in order to enable the wireless communicating nodes to efficiently utilize their energy resources. Different variations of the proposed schemes are presented where the energy benefit is specified. The performance evaluations through conducted experiments were performed in real-time, through wireless sensor nodes, and through simulation presenting the effectiveness of the framework which efficiently maximizes the reliability of the resource exchange process of the nodes, while it minimizes the energy consumption and prolongs the system’s lifetime.
KeywordsEnergy Conservation Scheduling management Energy level selfcontrol Layered-based Energy Conservation State-based Energy conservation Mobile Peer to Peer Networks energy management scheme High Resource Availability Traffic management and composition One-level Backward Traffic Difference scheme Selective Two-level Backward Traffic Difference scheme Traffic-oriented Energy Conservation Traffic Volume and Capacity metrics
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- 1.Mavromoustakis, C.: Using Backward Traffic Difference Estimation for Efficient Energy Saving Schedules in Wireless Device. IEEE CommSoft E-Letters 1(1), 1–6 (2012)Google Scholar
- 2.Mavromoustakis, C., Karatza, H.: Quality of Service Measures of Mobile Ad Hoc Wireless Network using Energy Consumption Mitigation with Asynchronous Inactivity Periods. Simulation: Transactions of the Society for Modelling and Simulation International 83, 107–122Google Scholar
- 4.Charalambous, M.C., Mavromoustakis, C.X., Muneer, B.Y.: A Resource Intensive Traffic-Aware Scheme for Cluster-based Energy Conservation in Wireless Devices. In: Proceedings IEEE 14th International Conference on High Performance Computing and Communications (HPCC 2012) of the Third International Workshop on Wireless Networks and Multimedia (WNM 2012), to be Held in Conjunction, June 25-27 (2012)Google Scholar
- 6.Mavromoustakis, C.: Mitigating file-sharing misbehavior with movement synchronization to increase end-to-end availability for delay sensitive streams in vehicular P2P devices. International Journal of Communication Systems (2013) (accepted)Google Scholar
- 7.Younis, O., Fahmy, S.: Distributed clustering in ad-hoc sensor networks: a hybrid, energy-efficient approach. In: Proceedings of IEEE INFOCOM 2004, Hong Kong, China (2004)Google Scholar
- 8.Mavromoustakis, C.: On the impact of caching and a model for storage-capacity measurements for energy conservation in asymmetrical wireless devices. In: 16th International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2008), IEEE Communication Society (COMSOC), September 25 & 26, pp. 243–247 (2008); “Dubrovnik”, September 27, Split and DubrovnikGoogle Scholar
- 10.Feeney, L., Nilsson, M.: Investigating the energy consumption of a wireless network interface in an ad hoc networking environment. In: Proc. of IEEE InfoCom, vol. 5(8) (2001)Google Scholar
- 12.Sheluhin, O., Smolskiy, S., Osin, A.: Self-Similar Processes in Telecommunications, p. 334 (2007) ISBN: 978-0-470-01486-8Google Scholar
- 13.Mastorakis, G., Mavromoustakis, C.X., Bourdena, A., Pallis, E.: An Energy-Efficient Routing Scheme using Backward Traffic Difference Estimation in Cognitive Radio Networks. In: Third IEEE Workshop on Convergence among Heterogeneous Wireless Systems in Future Internet (CONWIRE 2013) in Conjunction, IEEE WoWMoM 2013, The Fourteenth International Symposium on a World of Wireless, Mobile and Multimedia Networks, Madrid, Spain, June 4-7 (2013)Google Scholar
- 14.Mastorakis, G., Bourdena, A., Mavromoustakis, C., Pallis, E., Kormentzas, G.: An Energy-efficient Routing Protocol for Ad-hoc Cognitive Radio Networks. In: Cunningham, P., Cunningham, M. (eds.) Future Network &MobileSummit 2013 Conference Proceedings, p. 10. IIMC International Information Management Corporation (2013) ISBN: 978-1-905824-36-6Google Scholar